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372 lines
12 KiB
C++
372 lines
12 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include <test_precomp.hpp>
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#include <time.h>
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#ifdef HAVE_CUDA
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using cv::gpu::GpuMat;
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// show detection results on input image with cv::imshow
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#define SHOW_DETECTIONS
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#if defined SHOW_DETECTIONS
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# define SHOW(res) \
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cv::imshow(#res, result);\
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cv::waitKey(0);
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#else
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# define SHOW(res)
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#endif
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#define GPU_TEST_P(fixture, name, params) \
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class fixture##_##name : public fixture { \
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public: \
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fixture##_##name() {} \
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protected: \
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virtual void body(); \
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}; \
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TEST_P(fixture##_##name, name /*none*/){ body();} \
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INSTANTIATE_TEST_CASE_P(/*none*/, fixture##_##name, params); \
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void fixture##_##name::body()
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namespace {
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typedef cv::gpu::SCascade::Detection Detection;
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static cv::Rect getFromTable(int idx)
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{
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static const cv::Rect rois[] =
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{
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cv::Rect( 65 * 4, 20 * 4, 35 * 4, 80 * 4),
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cv::Rect( 95 * 4, 35 * 4, 45 * 4, 40 * 4),
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cv::Rect( 45 * 4, 35 * 4, 45 * 4, 40 * 4),
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cv::Rect( 25 * 4, 27 * 4, 50 * 4, 45 * 4),
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cv::Rect(100 * 4, 50 * 4, 45 * 4, 40 * 4),
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cv::Rect( 60 * 4, 30 * 4, 45 * 4, 40 * 4),
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cv::Rect( 40 * 4, 55 * 4, 50 * 4, 40 * 4),
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cv::Rect( 48 * 4, 37 * 4, 72 * 4, 80 * 4),
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cv::Rect( 48 * 4, 32 * 4, 85 * 4, 58 * 4),
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cv::Rect( 48 * 4, 0 * 4, 32 * 4, 27 * 4)
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};
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return rois[idx];
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}
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static std::string itoa(long i)
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{
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static char s[65];
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sprintf(s, "%ld", i);
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return std::string(s);
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}
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static std::string getImageName(int level)
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{
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time_t rawtime;
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struct tm * timeinfo;
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char buffer [80];
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time ( &rawtime );
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timeinfo = localtime ( &rawtime );
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strftime (buffer,80,"%Y-%m-%d--%H-%M-%S",timeinfo);
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return "gpu_rec_level_" + itoa(level)+ "_" + std::string(buffer) + ".png";
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}
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static void print(std::ostream &out, const Detection& d)
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{
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out << "\x1b[32m[ detection]\x1b[0m ("
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<< std::setw(4) << d.x
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<< " "
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<< std::setw(4) << d.y
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<< ") ("
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<< std::setw(4) << d.w
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<< " "
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<< std::setw(4) << d.h
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<< ") "
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<< std::setw(12) << d.confidence
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<< std::endl;
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}
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static void printTotal(std::ostream &out, int detbytes)
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{
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out << "\x1b[32m[ ]\x1b[0m Total detections " << (detbytes / sizeof(Detection)) << std::endl;
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}
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static void writeResult(const cv::Mat& result, const int level)
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{
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std::string path = cv::tempfile(getImageName(level).c_str());
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cv::imwrite(path, result);
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std::cout << "\x1b[32m" << "[ ]" << std::endl << "[ stored in]"<< "\x1b[0m" << path << std::endl;
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}
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}
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typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestRoi;
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GPU_TEST_P(SCascadeTestRoi, detect,
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testing::Combine(
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ALL_DEVICES,
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testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
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testing::Range(0, 5)))
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{
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cv::gpu::setDevice(GET_PARAM(0).deviceID());
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
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ASSERT_FALSE(coloredCpu.empty());
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cv::gpu::SCascade cascade;
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cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
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GpuMat colored(coloredCpu), objectBoxes(1, 16384, CV_8UC1), rois(colored.size(), CV_8UC1), trois;
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rois.setTo(0);
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int nroi = GET_PARAM(3);
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cv::Mat result(coloredCpu);
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cv::RNG rng;
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for (int i = 0; i < nroi; ++i)
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{
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cv::Rect r = getFromTable(rng(10));
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GpuMat sub(rois, r);
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sub.setTo(1);
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cv::rectangle(result, r, cv::Scalar(0, 0, 255, 255), 1);
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}
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objectBoxes.setTo(0);
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cascade.genRoi(rois, trois);
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cascade.detect(colored, trois, objectBoxes);
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cv::Mat dt(objectBoxes);
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typedef cv::gpu::SCascade::Detection Detection;
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Detection* dts = ((Detection*)dt.data) + 1;
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int* count = dt.ptr<int>(0);
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printTotal(std::cout, *count);
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for (int i = 0; i < *count; ++i)
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{
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Detection d = dts[i];
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print(std::cout, d);
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
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}
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SHOW(result);
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}
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typedef ::testing::TestWithParam<std::tr1::tuple<cv::gpu::DeviceInfo, std::string, std::string, int> > SCascadeTestLevel;
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GPU_TEST_P(SCascadeTestLevel, detect,
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testing::Combine(
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ALL_DEVICES,
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testing::Values(std::string("cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml")),
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testing::Values(std::string("../cv/cascadeandhog/bahnhof/image_00000000_0.png")),
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testing::Range(0, 47)
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))
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{
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cv::gpu::setDevice(GET_PARAM(0).deviceID());
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cv::gpu::SCascade cascade;
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cv::FileStorage fs(perf::TestBase::getDataPath(GET_PARAM(1)), cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path() + GET_PARAM(2));
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ASSERT_FALSE(coloredCpu.empty());
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typedef cv::gpu::SCascade::Detection Detection;
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GpuMat colored(coloredCpu), objectBoxes(1, 100 * sizeof(Detection), CV_8UC1), rois(colored.size(), CV_8UC1);
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rois.setTo(1);
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cv::gpu::GpuMat trois;
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cascade.genRoi(rois, trois);
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objectBoxes.setTo(0);
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int level = GET_PARAM(3);
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cascade.detect(colored, trois, objectBoxes, level);
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cv::Mat dt(objectBoxes);
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Detection* dts = ((Detection*)dt.data) + 1;
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int* count = dt.ptr<int>(0);
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cv::Mat result(coloredCpu);
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printTotal(std::cout, *count);
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for (int i = 0; i < *count; ++i)
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{
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Detection d = dts[i];
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print(std::cout, d);
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cv::rectangle(result, cv::Rect(d.x, d.y, d.w, d.h), cv::Scalar(255, 0, 0, 255), 1);
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}
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writeResult(result, level);
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SHOW(result);
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}
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TEST(SCascadeTest, readCascade)
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{
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/icf-template.xml";
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cv::gpu::SCascade cascade;
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cv::FileStorage fs(xml, cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
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}
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typedef ::testing::TestWithParam<cv::gpu::DeviceInfo > SCascadeTestAll;
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GPU_TEST_P(SCascadeTestAll, detect,
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ALL_DEVICES
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)
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{
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cv::gpu::setDevice(GetParam().deviceID());
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SCascade cascade;
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cv::FileStorage fs(xml, cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
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+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
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ASSERT_FALSE(coloredCpu.empty());
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GpuMat colored(coloredCpu), objectBoxes(1, 100000, CV_8UC1), rois(colored.size(), CV_8UC1);
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rois.setTo(0);
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GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
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sub.setTo(cv::Scalar::all(1));
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cv::gpu::GpuMat trois;
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cascade.genRoi(rois, trois);
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objectBoxes.setTo(0);
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cascade.detect(colored, trois, objectBoxes);
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typedef cv::gpu::SCascade::Detection Detection;
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cv::Mat detections(objectBoxes);
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int a = *(detections.ptr<int>(0));
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ASSERT_EQ(a ,2460);
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}
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GPU_TEST_P(SCascadeTestAll, detectOnIntegral,
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ALL_DEVICES
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)
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{
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cv::gpu::setDevice(GetParam().deviceID());
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SCascade cascade;
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cv::FileStorage fs(xml, cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
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std::string intPath = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/integrals.xml";
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cv::FileStorage fsi(intPath, cv::FileStorage::READ);
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ASSERT_TRUE(fsi.isOpened());
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GpuMat hogluv(121 * 10, 161, CV_32SC1);
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for (int i = 0; i < 10; ++i)
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{
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cv::Mat channel;
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fsi[std::string("channel") + itoa(i)] >> channel;
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GpuMat gchannel(hogluv, cv::Rect(0, 121 * i, 161, 121));
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gchannel.upload(channel);
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}
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GpuMat objectBoxes(1, 100000, CV_8UC1), rois(cv::Size(640, 480), CV_8UC1);
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rois.setTo(1);
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cv::gpu::GpuMat trois;
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cascade.genRoi(rois, trois);
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objectBoxes.setTo(0);
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cascade.detect(hogluv, trois, objectBoxes);
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typedef cv::gpu::SCascade::Detection Detection;
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cv::Mat detections(objectBoxes);
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int a = *(detections.ptr<int>(0));
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ASSERT_EQ( a ,1024);
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}
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GPU_TEST_P(SCascadeTestAll, detectStream,
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ALL_DEVICES
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)
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{
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cv::gpu::setDevice(GetParam().deviceID());
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std::string xml = cvtest::TS::ptr()->get_data_path() + "../cv/cascadeandhog/sc_cvpr_2012_to_opencv.xml";
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cv::gpu::SCascade cascade;
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cv::FileStorage fs(xml, cv::FileStorage::READ);
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ASSERT_TRUE(fs.isOpened());
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ASSERT_TRUE(cascade.load(fs.getFirstTopLevelNode()));
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cv::Mat coloredCpu = cv::imread(cvtest::TS::ptr()->get_data_path()
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+ "../cv/cascadeandhog/bahnhof/image_00000000_0.png");
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ASSERT_FALSE(coloredCpu.empty());
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GpuMat colored(coloredCpu), objectBoxes(1, 100000, CV_8UC1), rois(colored.size(), CV_8UC1);
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rois.setTo(0);
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GpuMat sub(rois, cv::Rect(rois.cols / 4, rois.rows / 4,rois.cols / 2, rois.rows / 2));
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sub.setTo(cv::Scalar::all(1));
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cv::gpu::Stream s;
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cv::gpu::GpuMat trois;
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cascade.genRoi(rois, trois, s);
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objectBoxes.setTo(0);
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cascade.detect(colored, trois, objectBoxes, s);
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cudaDeviceSynchronize();
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typedef cv::gpu::SCascade::Detection Detection;
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cv::Mat detections(objectBoxes);
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int a = *(detections.ptr<int>(0));
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ASSERT_EQ(a ,2460);
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}
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#endif |